The bones sat quietly in a fossil collection for years. One was part of a femur; the other, a tibia. They looked like the remains of an unusually large ancient human. But who did they belong to?

The answer came from an unexpected source: ancient proteins.

Using palaeoproteomic analysis, researchers found a molecular signature in both bones that matches the Denisovan lineage. The discovery identifies the two Penghu fossils as Denisovan and, more importantly, gives scientists their first substantial glimpse of the Denisovans' body size.

And the picture is striking.

One individual is estimated to have been about 1.8 metres tall and weighed around 83 kilograms. The other may have reached 1.9 metres and about 91 kilograms. Their leg bones rank among the largest known from Pleistocene Homo.

These were not small or fragile people.

They were powerful, heavily built humans moving through Ice Age eastern Asia.

But the bones tell an even more intriguing story. The femur has a pronounced ridge called a femoral pilaster—a feature particularly associated with modern human hunter-gatherers and increased mechanical strength during terrestrial movement. Why would a Denisovan, with an otherwise strongly archaic skeleton, possess this modern-looking feature?

Perhaps these Denisovans travelled extensively across the landscape. Perhaps their bodies were shaped by a demanding hunting lifestyle. Or perhaps, the researchers suggest, genetic exchange with early modern humans contributed to some of these features.

Their extraordinary size raises another mystery. A common expectation in human evolution is that populations living closer to the tropics tend to be smaller. Yet these Denisovans lived around 23°N latitude and were exceptionally large. The researchers argue that cold climate alone cannot explain their size, pointing instead toward lifestyle and diet—including evidence that at least one Penghu individual relied heavily on meat.

So, piece by piece, the Denisovans are becoming less mysterious.

What was once a shadowy population known mainly from DNA is beginning to take physical form: large, robust, mobile humans who lived across eastern Asia and whose bodies carried a fascinating mixture of ancient and modern traits.

And perhaps the most remarkable part of this story is where it began—not in a spectacular cave discovery, but with two weathered bones lying among thousands of fossils.

The Denisovans may have left no written history. But their bones are beginning to tell one.

*Note: This research is currently a bioRxiv preprint and has not yet undergone peer review.  Detail at https://www.biorxiv.org/content/10.64898/2026.08.07.743438v1.full.pdf

For use case 1 we obtained the following ENCODE and ROADMAP datasets https://www.encodeproject.org/files/ENCFF446WOD/@@download/ENCFF446WOD.bed.gz, https://www.encodeproject.org/files/ENCFF546PJU/@@download/ENCFF546PJU.bam, https://www.encodeproject.org/files/ENCFF059BEU/@@download/ENCFF059BEU.bam. Blacklisted regions were obtained from http://mitra.stanford.edu/kundaje/akundaje/release/blacklists/hg38-human/hg38.blacklist.bed.gz. The human genome version hg38 was obtained from http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz.

For use case 2 we used the set of narrowPeak files summarized in https://github.com/wkopp/janggu_usecases/tree/master/extra/urls.txt (archived version v1.0.1). The human genome version hg19 was obtained from http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/hg19.fa.gz

For use case 3 we used the ENCODE datasets https://www.encodeproject.org/files/ENCFF591XCX/@@download/ENCFF591XCX.bam, https://www.encodeproject.org/files/ENCFF736LHE/@@download/ENCFF736LHE.bigWig, https://www.encodeproject.org/files/ENCFF177HHM/@@download/ENCFF177HHM.bam as we as the GENCODE annotation v29 from ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_29/gencode.v29.annotation.gtf.gz.

Address of the bookmark: http://mitra.stanford.edu/

Scientists have reconstructed the genome of an ancient human who lived nearly 5,700 years ago in Southern Denmark from the birch pitch- an ancient tar-like substance.

By sequencing the sample, researchers not only discovered the ancient human DNA but also microbial DNA reflecting the oral microbiome of the person who chewed the pitch, along with plant and animal DNA that could be the recent meal she might have consumed.

The DNA sample is comparable in quality to well-preserved teeth and skull bones. The DNA suggests that the chewer was a female, most likely with dark skin, dark brown hair and blue eyes.

What Are Chromosome Rearrangements?

Chromosome rearrangements are structural changes that alter the normal configuration of chromosomes. These changes can involve large segments of DNA — from thousands to millions of base pairs — and can occur spontaneously, be inherited, or result from exposure to mutagens (like radiation or chemicals).

Common Types of Rearrangements:

1. Deletions – Loss of a chromosome segment

2. Duplications – Repetition of a segment

3. Inversions – A segment breaks off, flips, and reattaches

4. Translocations – Segments exchange places between non-homologous chromosomes

5. Insertions – A segment is inserted into another part of the genome

These changes can disrupt genes directly or affect gene regulation, leading to disease.

How Do Chromosome Rearrangements Cause Disease?

The impact of a rearrangement depends on which genes are involved, how much DNA is affected, and when the rearrangement occurs (in development vs. adulthood). Here are some key mechanisms:

• Gene disruption: Breaking a gene can lead to loss of function or the creation of a non-functional protein.

• Gene fusion: Joining parts of two genes may form a novel hybrid gene with new functions (common in cancer).

• Dosage effects: Extra or missing gene copies can disturb the balance of gene expression.

• Position effects: Moving a gene to a new regulatory environment may silence or over-activate it.

Chromosome Rearrangements in Human Disease

1. Developmental Disorders

• Cri-du-chat syndrome: Caused by a deletion on chromosome 5p. Affected infants often have a high-pitched cry and intellectual disability.

• Williams syndrome: Results from a microdeletion on chromosome 7q, affecting genes related to cardiovascular and cognitive function.

2. Cancer

Cancer is perhaps the most striking example of disease caused by chromosome rearrangements.

• Chronic Myeloid Leukemia (CML): Caused by a translocation between chromosomes 9 and 22, forming the Philadelphia chromosome. This creates the BCR-ABL fusion gene, which drives uncontrolled cell growth.

• Burkitt lymphoma: Involves translocation of the MYC gene, leading to excessive cell division.

• Ewing sarcoma: A fusion of EWSR1 and FLI1 genes through translocation promotes tumor development.

3. Infertility and Miscarriages

Balanced rearrangements (like inversions or translocations) in carriers may not cause disease directly but can result in:

• Recurrent miscarriages

• Infertility

• Birth defects in offspring

Detecting Rearrangements

Thanks to modern genomics, chromosome rearrangements can now be detected with high precision using:

• Karyotyping – Classic method for detecting large rearrangements

• FISH (Fluorescence In Situ Hybridization) – Uses fluorescent probes to target specific DNA sequences

• Array CGH (Comparative Genomic Hybridization) – Detects copy number changes across the genome

• Whole Genome Sequencing (WGS) – Identifies even small or complex rearrangements at base-pair resolution

Looking Forward: The Future of Chromosome Medicine

Understanding chromosome rearrangements is now central to:

• Personalized medicine

• Genetic counseling

• Targeted therapies, especially in cancer (e.g., tyrosine kinase inhibitors for BCR-ABL fusion)

With the rise of long-read sequencing and single-cell genomics, even previously “invisible” rearrangements are being uncovered, offering new insights into both rare diseases and common conditions.

Final Thoughts

Chromosome rearrangements remind us that genetics isn't just about which genes we have — but where they are, how they're arranged, and when they're active. As our tools grow sharper, so does our ability to diagnose, understand, and treat diseases rooted in genomic architecture.

In a way, the genome is like a book not just defined by its words, but also by how the chapters are ordered. Rearranging them can create a new story — sometimes harmful, sometimes insightful — and understanding these changes is key to writing a healthier future.

Dot supports the output of MUMmer’s nucmer aligner the most commonly used software method for aligning genome assemblies. A quick script called DotPrep.py converts the delta file to a more streamlined coordinates file with an index that enables Dot to read in more alignments in certain regions on demand.

Dot, an interactive viewer for genome-genome comparison

https://dnanexus.github.io/dot/

Address of the bookmark: https://github.com/dnanexus/dot

Features

• Interactive panning/zooming
• Compose maps using arbitrary combinations of:
  • Map tiles
  • Markers
  • Polygons
  • Lines
  • Popups
  • GeoJSON
• Create maps right from the R console or RStudio
• Embed maps in knitr/R Markdown documents and Shiny apps
• Easily render spatial objects from the sp or sf packages, or data frames with latitude/longitude columns
• Use map bounds and mouse events to drive Shiny logic
• Display maps in non spherical mercator projections
• Augment map features using chosen plugins from leaflet plugins repository

https://rstudio.github.io/leaflet/

Address of the bookmark: https://rstudio.github.io/leaflet/

https://academic.oup.com/bioinformaticsadvances/advance-article/doi/10.1093/bioadv/vbaf208/8257678?login=true

Address of the bookmark: https://github.com/sib-swiss/termal

Below you can find a few basic tutorials for how to run MITObim and I encorage you to give them a try with the testdata that comes with this Repo, just to make sure everything is running smoothly on your system. It'll only take a few minutes and will potentially safe you a lot of time down the line.

I provide further examples here as Jupyter notebooks. Get in touch if you feel like sharing your particular MITObim solution and I'd be happy to put it up here, too!

Address of the bookmark: https://github.com/chrishah/MITObim

1) expression-based heat maps from transcriptomic, proteomic and metabolomic experiments; 2) pairwise distance maps;

3) correlation maps;

4) image overlay heat maps;

5) latitude and longitude heat maps and

6) geopolitical (choropleth) heat maps.

Heatmapper offers a number of simple and intuitive customization options for easy adjustments to each heat map’s appearance and plotting parameters. Heatmapper also allows users to interactively explore their numeric data values by hovering their cursor over each heat map, or by using a searchable/sortable data table view.

Ref https://www.ncbi.nlm.nih.gov/pubmed/27190236

Address of the bookmark: http://www2.heatmapper.ca/

The Indian scientists working in Bangalore, along with their American counterparts, have mapped more than 17,000 proteins in 30 organs of the human body. Just like the human genome was sequenced around the turn of the millennium, this is an equivalent mapping of the human proteome.

The researcher estimated there are around 20,500 proteins in the human body. These scientists have profiled around 17,294, which account for around 84% of the total proteins. Apart from this, the team also traced around 2,500 of 3,000 proteins that had been categorised as "missing proteins".

The work, done by group of Indian scientists, and Johns Hopkins University, published in the renowned journal Nature ( http://www.nature.com/nature/journal/v509/n7502/full/nature13302.html ). Of the 72 people who worked on the project, 46 are Indians.

Reference:

http://www.nature.com/nature/journal/v509/n7502/full/nature13302.html

http://www.proteinatlas.org/ -The antibody-based Human Protein Atlas programme

http://www.humanproteomemap.org/ -Proteogenomic analysis by identifying translated proteins from annotated pseudogenes, non-coding RNAs and untranslated regions.

https://www.proteomicsdb.org/ -Assembled protein evidence for 18,097 genes in ProteomicsDB